Shahrood University of Technology Journal of Mining and Environment 2251-8592 8 3 2017 07 01 Predicting tensile strength of rocks from physical properties based on support vector regression optimized by cultural algorithm 467 474 824 10.22044/jme.2016.824 EN H. Fattahi Department of Mining Engineering, Arak University of Technology, Arak, Iran N. Babanouri Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran Journal Article 2016 11 11 The tensile strength (TS) of rocks is an important parameter in the design of a variety of engineering structures such as the surface and underground mines, dam foundations, types of tunnels and excavations, and oil wells. In addition, the physical properties of a rock are intrinsic characteristics, which influence its mechanical behavior at a fundamental level. In this paper, a new approach combining the support vector regression (SVR) with a cultural algorithm (CA) is presented in order to predict TS of rocks from their physical properties. CA is used to determine the optimal value of the SVR controlling the parameters. A dataset including 29 data points was used in this study, in which 20 data points (70%) were considered for constructing the model and the remaining ones (9 data points) were used to evaluate the degree of accuracy and robustness. The results obtained show that the SVR optimized by the CA model can be successfully used to predict TS. https://jme.shahroodut.ac.ir/article_824_e2745cfae2a5d03e40c47022a568e65c.pdf